Migration

CloudSuite Industrial Data Migration Plan

Migrating data from on-premise SyteLine to CloudSuite Industrial requires a structured approach covering data profiling, cleansing, transformation, and validation across all functional modules. The migration typically spans 8-16 weeks depending on data volume and complexity, with the actual cutover window compressed to 48-72 hours. This guide provides a comprehensive migration plan framework with real table mappings and validation checkpoints.

Data Profiling and Cleansing Strategy

Before any migration tooling is configured, a thorough data profiling exercise must catalog every table in the source SyteLine database, assess data quality metrics, and identify records requiring cleansing or archival. Key profiling targets include the item master (item table), customer/vendor records (custaddr/vendaddr), BOM structures (jobmatl/jobroute), and open transaction tables (coitem, poitem, job). Infor provides the Data Migration Workbench (DMW) as the primary tool, but many organizations supplement with custom SQL scripts for complex transformations.

  • Profile all core tables: item, custaddr, vendaddr, jobmatl, jobroute, coitem, poitem, job, and wh/loc records
  • Identify orphaned records, duplicate customers/vendors, and inactive items for archival before migration
  • Validate referential integrity between master data (items, customers) and transactional data (orders, jobs)
  • Assess custom table extensions and user-defined fields that require mapping to CSI UDF framework
  • Generate data quality scorecards with completeness, accuracy, and consistency metrics per module

ETL Pipeline and Transformation Rules

The ETL pipeline extracts data from the source SQL Server database, transforms it according to CSI target schema requirements, and loads it through Infor Data Migration Workbench or ION API bulk import endpoints. Transformation rules handle schema differences between on-premise SyteLine versions (8.x, 9.x, 10.x) and CSI cloud, including field length changes, data type conversions, and deprecated field remapping. Critical transformations include GL account structure mapping, unit of measure consolidation, and address format standardization.

  • Extract using SQL Server Integration Services (SSIS) or custom Python scripts with pyodbc for source database access
  • Transform GL account segments to match CSI chart of accounts structure using the account_map migration table
  • Consolidate unit of measure codes across items, ensuring u_m values match CSI's standardized UOM master
  • Load master data first (items, customers, vendors, warehouses) then open transactions (orders, jobs, AR/AP)
  • Use ION API bulk import endpoints (/M3/M3_Sync/) for high-volume transaction loading with retry logic

Validation Framework and Cutover Procedures

Post-load validation compares record counts, financial balances, and inventory valuations between source and target systems. A three-phase validation approach ensures completeness: Phase 1 validates master data counts and key field accuracy, Phase 2 reconciles open transaction balances (AR, AP, GL, inventory value), and Phase 3 performs end-to-end transaction testing in the CSI environment. The cutover procedure freezes the source system, runs a final delta migration, validates balances, and enables CSI for production use.

  • Phase 1: Record count validation across all migrated tables with field-level sampling for data accuracy
  • Phase 2: Financial reconciliation comparing GL trial balance, AR/AP aging, and inventory valuation totals
  • Phase 3: End-to-end transaction testing including purchase receipt, production completion, and sales shipment
  • Delta migration script captures transactions entered during the cutover freeze window for final load
  • Go/No-Go decision checkpoint at T-4 hours with rollback procedure documented and tested in advance

Need a detailed migration plan for your SyteLine-to-CSI project? Netray delivers proven migration frameworks with automated validation tooling.